MindRACES Demo Movies

Analogical Aibo

The video shows an AIBO robot which is able to find objects hidden behind shelters by reasoning by analogy with previously experienced situations. This is an example of an ‘embodied’ model of analogy, since the robot is able to form internal representations based on its perception, to manipulate these representations (make analogies) and therefore to move on the basis of the analogical prediction.

Guard and thief

The video shows the navigation and planning capabilities of a simulated robot playing the role of a thief in a 3D guards-and-thieves scenario. The robot architecture is layered. The lower (sensorimotor) layer manages is composed of multiple anticipatory schemas (e.g., detect treasure, escape guard) which compete for being executed. The higher (deliberative) layer plans on the basis of the robot’s goals (find the treasure and escape the guard) and of beliefs which are derived on the fly on the basis of the schema’s success or unsuccess in prediction.

Sure moving arm (1 and 2)

These two videos (1 and 2) show the SURE_REACH architecture for flexible goal-directed control of action. SURE REACH (a loose acronym for Sensorimotor,Unsupervised, REdundancy-REsolving control ArCHchitecture) is a hierarchically structured control architecture, which builds its internal representations from scratch. Initially, it explores its environment by means of random motor babbling. The knowledge of SURE REACH about its body and environment consists of two population-encoded sp atial body representations (the neurons of the population code are currently uniformly distributed in space, adaptive spatial coverage methods are being investigated) and two associative structures (learned). SURE REACH has been applied to the control of a 3-DOF arm in a 2-D environment so that each target position can be reached with various goal postures and on various paths.

Anticipatory Robotic Hand

The video
illustrates a demo on the functioning
of a neural-network architecture which controls a robot composed of a
webcam and a 2-link 4-DOF robotic arm engaged in a reaching task. The
demo shows the work carried out within the EU funded project
MindRACES with the aim of integrating attention models and
arm-control models respectively developed by LUCS and CNR-ISTC within
the project. The web-cam looks at the arm from top. The arm acts on a
working plane formed by a computer screen that projects various trees
items in a sequence of multiple learning trials. A tree is formed by
various coloured squares: green squares for the foliage, blue squares
for the trunk and red squares for the apple (the target of the arm).
Even if the webcam is still, at each time step a pixel sub region is
extracted from the camera image to mimic a moving eye composed of (a)
a small fovea which perceives the colour of the fixated square and
(b) a periphery that perceives only the presence/absence of squares
surrounding the fovea (it perceives them as grey). The task of the
system is to learn to move the eye on relevant parts of the image -
in particular to learn to look below the foliage, and aside the
trunk, as the apples grow there - and to keep the eye on the apple,
once this is found, for multiple saccades: this triggers an arms
reaching movement (see below). The eye movement is controlled by an
architecture composed of neural maps which encode information in
eye-centred coordinate frames. These maps implement: (a) a neural
competition between alternative locations that the fovea might
fixate; (b) a bottom-up attention process which leads the fovea on
regions with high contrasts; (c) a top-down attention process that
learns, by reinforcement learning, to guide the fovea on regions with
high information gain: this part is also capable of accumulating - in
a potential action map, the most innovative component of the system -
information about multiple promising possible saccade targets on the
basis of the various objects (squares) fixated in time. The arm is
controlled by a neural biased-competition, fuelled by the current
proprioception of the eyes gaze direction, which selects possible
targets for the arm-reaching movements and triggers them when the eye
fixates for a long time the same spot. Once the target for reaching
is selected, this is transformed into the corresponding desired arm
posture by a previously-trained neural network implementing an
inverse model, and then this posture is issued to the arm motors for
execution. Importantly, the reinforcement learning of the top-down
attention system is guided by the reward obtained by the arm
movement: in this respect the model integrates epistemic and
pragmatic actions in an unprecedented fashion. The systems
functioning is based on various anticipatory mechanisms: (a) the
bottom-up attention component anticipates potentially interesting
targets for the epistemic eye movements; (b) the top-down attention
component learns to anticipate potential interesting eye targets and
builds a dynamic mapping of their locations; (c) both the eye and the
arm are not controlled on the basis of movements but on the basis of
actions goals (i.e. desired anticipated states) in line with the
ideomotor principle.

Anticipatory Grasping Hand

The video shows a robotic arm which is able to adapt to several sensorimotor contexts and grasp objects of different sizes and weight (in this case, balloons remplished with water) by autonomousy arbitraring among different anticipatory schemas ono the basis of their prediction error.

Anticipatory Cognitive Science is a research field that ensembles artificial intelligence,
biology, psychology, neurology, engineering and philosophy in order to build anticipatory cognitive systems that
are able to face human tasks with the same anticipatory capabilities and performance. In deep:
Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology,
artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s
when researchers in several fields began to develop theories of mind based on complex representations and
computational procedures. Its organizational origins are in the mid-1970s when the Cognitive Science Society
was formed and the journal Cognitive Science began. Since then, more than sixty universities in North America, Europe, Asia,
and Australia have established cognitive science programs, and many others have instituted courses in cognitive science.